Abstract
In this paper, we present an automated method to segment blood vessels in fundus retinal images. The method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. Our method combines the bias correction to correct the intensity inhomogeneity of the retinal image, and a matched filter to enhance the appearance of the blood vessels. The blood vessels are then extracted from the matched filter response image using the Expectation Maximisation algorithm. The method is tested on fundus retinal images of STARE dataset and the experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.
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References
Fritzsche, K., Can, A., Shen, H., Tsai, C., Turner, J., Stewart, C., Roysam, B.: Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images (2003)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19(3), 203–210 (2002)
Mendonça, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25, 1200–1213 (2006)
Xu, L., Luo, S.: A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine 9(1), 1–10 (2010)
Salazar-Gonzalez, A., Li, Y., Kaba, D.: Mrf reconstruction of retinal images for the optic disc segmentation (2012)
Tustison, N., Avants, B., Cook, P., Zheng, Y., Egan, A., Yushkevich, P., Gee, J.: N4itk: Improved n3 bias correction. IEEE Transactions on Medical Imaging 29(6), 1310–1320 (2010)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Transactions on Medical Imaging 17(1), 87–97 (1998)
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging 8(3), 263–269 (1989)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38 (1977)
Clarke, L., Velthuizen, R., Camacho, M., Heine, J., Vaidyanathan, M., Hall, L., Thatcher, R., Silbiger, M.: Mri segmentation: Methods and applications. Magnetic Resonance Imaging 13(3), 343–368 (1995)
Pyun, K., Lim, J., Won, C., Gray, R.: Image segmentation using hidden markov gauss mixture models. IEEE Transactions on Image Processing 16(7), 1902–1911 (2007)
Chanwimaluang, T., Fan, G.: An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS 2003, vol. 5, pp. V–21. IEEE, Los Alamitos (2003)
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Kaba, D., Salazar-Gonzalez, A.G., Li, Y., Liu, X., Serag, A. (2013). Segmentation of Retinal Blood Vessels Using Gaussian Mixture Models and Expectation Maximisation. In: Huang, G., Liu, X., He, J., Klawonn, F., Yao, G. (eds) Health Information Science. HIS 2013. Lecture Notes in Computer Science, vol 7798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37899-7_9
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DOI: https://doi.org/10.1007/978-3-642-37899-7_9
Publisher Name: Springer, Berlin, Heidelberg
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